Our quantitative methods in education researchers are using statistics to change the way we look at how people learn.
Dr. Harwell (quantitative methods in education) focuses on developing meta-analytic effect sizes and tests that extract information from a pool of studies beyond traditional measures like standardized mean differences for two groups; examines the role and impact of conceptual and empirical models for socioeconomic status (SES); and examines nonparametric estimators and tests for complex statistical models particularly linear (mixed) models that serve as competitors to normal-theory-based methods.
Dr. delMas, and Zieffler (quantitative methods in education) are statistics education researchers investigating how students understand statistical concepts such as sampling variability and the logic of statistical inference. They are also developing innovative curricula for teaching statistics to college students from a modern, simulation-oriented perspective, as well as assessments for measuring students’ statistical reasoning and understanding.
Dr. Kohli (quantitative methods in education) researches the development and improvement of statistical methods for analyzing educational, psychological, and more generally social and behavioral sciences data, particularly longitudinal (repeated measures) data. The aim of this work is to move the educational statistics literature forward and provide researchers and practitioners with the theoretical underpinnings and empirical guidance to utilize these methods to address important substantive questions in education.
Concepts, principles, and methods in educational/psychological measurement. Reliability, validity, item analysis, scores, score reports (e.g., grades). Modern measurement theories, including item response theory and generalizability theory. Emphasizes construction, interpretation, use, and evaluation of assessments regarding achievement, aptitude, interests, attitudes, personality, and exceptionality.